import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()


def 	lowerCamelCase					(	UpperCamelCase     :				Dict			,       UpperCamelCase     :				Optional[int]			,       UpperCamelCase     :				str			,       UpperCamelCase     :				Union[str, Any]="attention"							)   ->    Optional[Any]:
		_lowerCamelCase				   =  params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
		_lowerCamelCase				   =  params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
		_lowerCamelCase				   =  params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
		_lowerCamelCase				   =  params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
		return k, o, q, v


def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				Tuple			,       UpperCamelCase     :				List[Any]			,       UpperCamelCase     :				Any=False							)   ->    List[str]:
		if split_mlp_wi:
				_lowerCamelCase				   =  params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
				_lowerCamelCase				   =  params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
				_lowerCamelCase				   =  (wi_a, wi_a)
		else:
				_lowerCamelCase				   =  params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

		_lowerCamelCase				   =  params[F"""{prefix}/layers_{i}/mlp/wo/kernel"""]
		return wi, wo


def 	lowerCamelCase					(	UpperCamelCase     :				Dict			,       UpperCamelCase     :				List[Any]			,       UpperCamelCase     :				int			,       UpperCamelCase     :				Tuple							)   ->    Optional[Any]:
		return params[F"""{prefix}/layers_{i}/{layer_name}/scale"""]


def 	lowerCamelCase					(	UpperCamelCase     :				dict			,       *, UpperCamelCase     :				int			,       UpperCamelCase     :				bool							)   ->    List[str]:
		_lowerCamelCase				   =  traverse_util.flatten_dict(variables['target']							)
		_lowerCamelCase				   =  {'/'.join(UpperCamelCase							): v for k, v in old.items()}

		# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
		_lowerCamelCase				   =  'encoder/layers_0/mlp/wi_0/kernel' in old
		print('Split MLP:'			,       UpperCamelCase							)

		_lowerCamelCase				   =  collections.OrderedDict()

		# Shared embeddings.
		_lowerCamelCase				   =  old['token_embedder/embedding']

		# Encoder.
		for i in range(UpperCamelCase							):
				# Block i, layer 0 (Self Attention).
				_lowerCamelCase				   =  tax_layer_norm_lookup(UpperCamelCase			,       UpperCamelCase			,       'encoder'			,       'pre_attention_layer_norm'							)
				_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  tax_attention_lookup(UpperCamelCase			,       UpperCamelCase			,       'encoder'			,       'attention'							)
				_lowerCamelCase				   =  layer_norm
				_lowerCamelCase				   =  k.T
				_lowerCamelCase				   =  o.T
				_lowerCamelCase				   =  q.T
				_lowerCamelCase				   =  v.T

				# Block i, layer 1 (MLP).
				_lowerCamelCase				   =  tax_layer_norm_lookup(UpperCamelCase			,       UpperCamelCase			,       'encoder'			,       'pre_mlp_layer_norm'							)
				_lowerCamelCase   ,							_lowerCamelCase				   =  tax_mlp_lookup(UpperCamelCase			,       UpperCamelCase			,       'encoder'			,       UpperCamelCase							)
				_lowerCamelCase				   =  layer_norm
				if split_mlp_wi:
						_lowerCamelCase				   =  wi[0].T
						_lowerCamelCase				   =  wi[1].T
				else:
						_lowerCamelCase				   =  wi.T
				_lowerCamelCase				   =  wo.T

		_lowerCamelCase				   =  old[
		    'encoder/relpos_bias/rel_embedding'
		].T
		_lowerCamelCase				   =  old['encoder/encoder_norm/scale']

		if not is_encoder_only:
				# Decoder.
				for i in range(UpperCamelCase							):
						# Block i, layer 0 (Self Attention).
						_lowerCamelCase				   =  tax_layer_norm_lookup(UpperCamelCase			,       UpperCamelCase			,       'decoder'			,       'pre_self_attention_layer_norm'							)
						_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  tax_attention_lookup(UpperCamelCase			,       UpperCamelCase			,       'decoder'			,       'self_attention'							)
						_lowerCamelCase				   =  layer_norm
						_lowerCamelCase				   =  k.T
						_lowerCamelCase				   =  o.T
						_lowerCamelCase				   =  q.T
						_lowerCamelCase				   =  v.T

						# Block i, layer 1 (Cross Attention).
						_lowerCamelCase				   =  tax_layer_norm_lookup(UpperCamelCase			,       UpperCamelCase			,       'decoder'			,       'pre_cross_attention_layer_norm'							)
						_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  tax_attention_lookup(UpperCamelCase			,       UpperCamelCase			,       'decoder'			,       'encoder_decoder_attention'							)
						_lowerCamelCase				   =  layer_norm
						_lowerCamelCase				   =  k.T
						_lowerCamelCase				   =  o.T
						_lowerCamelCase				   =  q.T
						_lowerCamelCase				   =  v.T

						# Block i, layer 2 (MLP).
						_lowerCamelCase				   =  tax_layer_norm_lookup(UpperCamelCase			,       UpperCamelCase			,       'decoder'			,       'pre_mlp_layer_norm'							)
						_lowerCamelCase   ,							_lowerCamelCase				   =  tax_mlp_lookup(UpperCamelCase			,       UpperCamelCase			,       'decoder'			,       UpperCamelCase							)
						_lowerCamelCase				   =  layer_norm
						if split_mlp_wi:
								_lowerCamelCase				   =  wi[0].T
								_lowerCamelCase				   =  wi[1].T
						else:
								_lowerCamelCase				   =  wi.T
						_lowerCamelCase				   =  wo.T

				_lowerCamelCase				   =  old['decoder/decoder_norm/scale']
				_lowerCamelCase				   =  old[
				    'decoder/relpos_bias/rel_embedding'
				].T

				# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
				if "decoder/logits_dense/kernel" in old:
						_lowerCamelCase				   =  old['decoder/logits_dense/kernel'].T

		return new


def 	lowerCamelCase					(	UpperCamelCase     :				Tuple			,       UpperCamelCase     :				bool							)   ->    Optional[Any]:
		_lowerCamelCase				   =  collections.OrderedDict([(k, torch.from_numpy(v.copy()							)) for (k, v) in converted_params.items()]							)

		# Add what is missing.
		if "encoder.embed_tokens.weight" not in state_dict:
				_lowerCamelCase				   =  state_dict['shared.weight']

		if not is_encoder_only:
				if "decoder.embed_tokens.weight" not in state_dict:
						_lowerCamelCase				   =  state_dict['shared.weight']

				if "lm_head.weight" not in state_dict:  # For old 1.0 models.
						print('Using shared word embeddings as lm_head.'							)
						_lowerCamelCase				   =  state_dict['shared.weight']

		return state_dict


def 	lowerCamelCase					(	UpperCamelCase     :				Tuple			,       UpperCamelCase     :				Optional[Any]			,       UpperCamelCase     :				Union[str, Any]			,       UpperCamelCase     :				List[Any]							)   ->    Dict:
		_lowerCamelCase				   =  checkpoints.load_tax_checkpoint(UpperCamelCase							)
		_lowerCamelCase				   =  convert_tax_to_pytorch(UpperCamelCase			,       num_layers=config.num_layers			,       is_encoder_only=UpperCamelCase							)
		_lowerCamelCase				   =  make_state_dict(UpperCamelCase			,       UpperCamelCase							)
		model.load_state_dict(UpperCamelCase			,       strict=UpperCamelCase							)


def 	lowerCamelCase					(	UpperCamelCase     :				Optional[int]			,       UpperCamelCase     :				str			,       UpperCamelCase     :				List[str]			,       UpperCamelCase     :				bool = False							)   ->    Dict:
		_lowerCamelCase				   =  TaConfig.from_json_file(UpperCamelCase							)
		print(F"""Building PyTorch model from configuration: {config}"""							)
		# Non-v1.1 checkpoints could also use T5Model, but this works for all.
		# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
		if is_encoder_only:
				_lowerCamelCase				   =  TaEncoderModel(UpperCamelCase							)
		else:
				_lowerCamelCase				   =  TaForConditionalGeneration(UpperCamelCase							)

		# Load weights from tf checkpoint
		load_tax_weights_in_ta(UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase							)

		# Save pytorch-model
		print(F"""Save PyTorch model to {pytorch_dump_path}"""							)
		model.save_pretrained(UpperCamelCase							)

		# Verify that we can load the checkpoint.
		model.from_pretrained(UpperCamelCase							)
		print('Done'							)


if __name__ == "__main__":
				A						=			argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
				# Required parameters
				parser.add_argument(
				    '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
				)
				parser.add_argument(
				    '--config_file',
				    default=None,
				    type=str,
				    required=True,
				    help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
				)
				parser.add_argument(
				    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
				)
				parser.add_argument(
				    '--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
				)
				A						=			parser.parse_args()
				convert_tax_checkpoint_to_pytorch(
				    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
				)
import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_     ,unittest.TestCase			):


		'''simple docstring'''






		lowerCAmelCase_     =   ShapEImgaImgPipeline
		lowerCAmelCase_     =   ['image']
		lowerCAmelCase_     =   ['image']
		lowerCAmelCase_     =   [
		    'num_images_per_prompt',
		    'num_inference_steps',
		    'generator',
		    'latents',
		    'guidance_scale',
		    'frame_size',
		    'output_type',
		    'return_dict',
		]
		lowerCAmelCase_     =   False
		@property
		def   _snake_case       (       self     :     List[str]		)      ->	Any:
				return 3_2
		@property
		def   _snake_case       (       self     :     int		)      ->	Dict:
				return 3_2
		@property
		def   _snake_case       (       self     :     Tuple		)      ->	Optional[Any]:
				return self.time_input_dim * 4
		@property
		def   _snake_case       (       self     :     Tuple		)      ->	Optional[Any]:
				return 8
		@property
		def   _snake_case       (       self     :     int		)      ->	Dict:
				torch.manual_seed(0		)
				_lowerCamelCase				   =  CLIPVisionConfig(
				    hidden_size=self.text_embedder_hidden_size     ,  image_size=6_4     ,  projection_dim=self.text_embedder_hidden_size     ,  intermediate_size=3_7     ,  num_attention_heads=4     ,  num_channels=3     ,  num_hidden_layers=5     ,  patch_size=1     ,  )

				_lowerCamelCase				   =  CLIPVisionModel(snake_case__		)
				return model
		@property
		def   _snake_case       (       self     :     Tuple		)      ->	Any:
				_lowerCamelCase				   =  CLIPImageProcessor(
				    crop_size=2_2_4     ,  do_center_crop=snake_case__     ,  do_normalize=snake_case__     ,  do_resize=snake_case__     ,  image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]     ,  image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]     ,  resample=3     ,  size=2_2_4     ,  )

				return image_processor
		@property
		def   _snake_case       (       self     :     List[str]		)      ->	Any:
				torch.manual_seed(0		)

				_lowerCamelCase				   =  {
				    'num_attention_heads': 2,
				    'attention_head_dim': 1_6,
				    'embedding_dim': self.time_input_dim,
				    'num_embeddings': 3_2,
				    'embedding_proj_dim': self.text_embedder_hidden_size,
				    'time_embed_dim': self.time_embed_dim,
				    'num_layers': 1,
				    'clip_embed_dim': self.time_input_dim * 2,
				    'additional_embeddings': 0,
				    'time_embed_act_fn': 'gelu',
				    'norm_in_type': 'layer',
				    'embedding_proj_norm_type': 'layer',
				    'encoder_hid_proj_type': None,
				    'added_emb_type': None,
				}

				_lowerCamelCase				   =  PriorTransformer(**snake_case__		)
				return model
		@property
		def   _snake_case       (       self     :     List[Any]		)      ->	Optional[int]:
				torch.manual_seed(0		)

				_lowerCamelCase				   =  {
				    'param_shapes': (
				        (self.renderer_dim, 9_3),
				        (self.renderer_dim, 8),
				        (self.renderer_dim, 8),
				        (self.renderer_dim, 8),
				    ),
				    'd_latent': self.time_input_dim,
				    'd_hidden': self.renderer_dim,
				    'n_output': 1_2,
				    'background': (
				        0.1,
				        0.1,
				        0.1,
				    ),
				}
				_lowerCamelCase				   =  ShapERenderer(**snake_case__		)
				return model
		def   _snake_case       (       self     :     Any		)      ->	str:
				_lowerCamelCase				   =  self.dummy_prior
				_lowerCamelCase				   =  self.dummy_image_encoder
				_lowerCamelCase				   =  self.dummy_image_processor
				_lowerCamelCase				   =  self.dummy_renderer

				_lowerCamelCase				   =  HeunDiscreteScheduler(
				    beta_schedule='exp'     ,  num_train_timesteps=1_0_2_4     ,  prediction_type='sample'     ,  use_karras_sigmas=snake_case__     ,  clip_sample=snake_case__     ,  clip_sample_range=1.0     ,  )
				_lowerCamelCase				   =  {
				    'prior': prior,
				    'image_encoder': image_encoder,
				    'image_processor': image_processor,
				    'renderer': renderer,
				    'scheduler': scheduler,
				}

				return components
		def   _snake_case       (       self     :     List[str]     ,  snake_case__     :     str     ,  snake_case__     :     Optional[Any]=0		)      ->	Union[str, Any]:
				_lowerCamelCase				   =  floats_tensor((1, 3, 6_4, 6_4)     ,  rng=random.Random(snake_case__		)		).to(snake_case__		)

				if str(snake_case__		).startswith('mps'		):
						_lowerCamelCase				   =  torch.manual_seed(snake_case__		)
				else:
						_lowerCamelCase				   =  torch.Generator(device=snake_case__		).manual_seed(snake_case__		)
				_lowerCamelCase				   =  {
				    'image': input_image,
				    'generator': generator,
				    'num_inference_steps': 1,
				    'frame_size': 3_2,
				    'output_type': 'np',
				}
				return inputs
		def   _snake_case       (       self     :     Union[str, Any]		)      ->	Union[str, Any]:
				_lowerCamelCase				   =  'cpu'

				_lowerCamelCase				   =  self.get_dummy_components()

				_lowerCamelCase				   =  self.pipeline_class(**snake_case__		)
				_lowerCamelCase				   =  pipe.to(snake_case__		)

				pipe.set_progress_bar_config(disable=snake_case__		)

				_lowerCamelCase				   =  pipe(**self.get_dummy_inputs(snake_case__		)		)
				_lowerCamelCase				   =  output.images[0]
				_lowerCamelCase				   =  image[0, -3:, -3:, -1]

				assert image.shape == (2_0, 3_2, 3_2, 3)

				_lowerCamelCase				   =  np.array(
				    [
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				    ]		)

				assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
		def   _snake_case       (       self     :     Dict		)      ->	Optional[Any]:
				# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
				self._test_inference_batch_consistent(batch_sizes=[1, 2]		)
		def   _snake_case       (       self     :     Optional[Any]		)      ->	Tuple:
				_lowerCamelCase				   =  torch_device == 'cpu'
				_lowerCamelCase				   =  True
				self._test_inference_batch_single_identical(
				    batch_size=2     ,  test_max_difference=snake_case__     ,  relax_max_difference=snake_case__     ,  )



		def   _snake_case       (       self     :     Any		)      ->	Tuple:
				_lowerCamelCase				   =  self.get_dummy_components()
				_lowerCamelCase				   =  self.pipeline_class(**snake_case__		)
				_lowerCamelCase				   =  pipe.to(snake_case__		)
				pipe.set_progress_bar_config(disable=snake_case__		)

				_lowerCamelCase				   =  1
				_lowerCamelCase				   =  2

				_lowerCamelCase				   =  self.get_dummy_inputs(snake_case__		)

				for key in inputs.keys():
						if key in self.batch_params:
								_lowerCamelCase				   =  batch_size * [inputs[key]]

				_lowerCamelCase				   =  pipe(**snake_case__     ,  num_images_per_prompt=snake_case__		)[0]

				assert images.shape[0] == batch_size * num_images_per_prompt





@slow
@require_torch_gpu
class     lowerCAmelCase__     (				unittest.TestCase			):


		'''simple docstring'''






		def   _snake_case       (       self     :     Union[str, Any]		)      ->	Optional[int]:
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



		def   _snake_case       (       self     :     List[str]		)      ->	List[Any]:
				_lowerCamelCase				   =  load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png'		)
				_lowerCamelCase				   =  load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/shap_e/test_shap_e_img2img_out.npy'		)
				_lowerCamelCase				   =  ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img'		)
				_lowerCamelCase				   =  pipe.to(snake_case__		)
				pipe.set_progress_bar_config(disable=snake_case__		)

				_lowerCamelCase				   =  torch.Generator(device=snake_case__		).manual_seed(0		)

				_lowerCamelCase				   =  pipe(
				    snake_case__     ,  generator=snake_case__     ,  guidance_scale=3.0     ,  num_inference_steps=6_4     ,  frame_size=6_4     ,  output_type='np'     ,  ).images[0]

				assert images.shape == (2_0, 6_4, 6_4, 3)

				assert_mean_pixel_difference(snake_case__     ,  snake_case__		)